Inhibitors of ApiAP2 protein DNA binding exhibit multistage activity against Plasmodium parasites.

Plasmodium parasites are reliant on the Apicomplexan AP2 (ApiAP2) transcription factor family to regulate gene expression programs. AP2 DNA binding domains have no homologs in the human or mosquito host genomes, making them potential antimalarial drug targets. Using an in-silico screen to dock thousands of small molecules into the crystal structure of the AP2-EXP (Pf3D7_1466400) AP2 domain (PDB:3IGM), we identified putative AP2-EXP interacting compounds.

Computational Analysis of LOX1 Inhibition Identifies Descriptors Responsible for Binding Selectivity.

Lipoxygenases are a family of cytosolic, peripheral membrane enzymes, which catalyze the hydroperoxidation of polyunsaturated fatty acids and are implicated in the pathogenesis of major human diseases. Over the years, a substantial number of scientific reports have introduced inhibitors active against one or another subtype of the enzyme, but the selectivity issue has proved to be a major challenge for drug design. In the present work, we assembled a dataset of 317 structurally diverse molecules hitherto reported as active against 15S-LOX1, 12S-LOX1, and 15S-LOX2 and identified, using supervised machine learning, a set of structural descriptors responsible for the binding selectivity toward the enzyme 15S-LOX1.

NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy.

http://sbb.hku.hk/services/NutriChem-2.

Analysis of free text in electronic health records for identification of cancer patient trajectories.

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004-2012.

Synthesis and biological evaluation of dihydropyrano-[2,3-c]pyrazoles as a new class of PPARγ partial agonists.

Peroxisome proliferator-activated receptor γ (PPARγ) is a well-known target for thiazolidinedione antidiabetic drugs. In this paper, we present the synthesis and biological evaluation of a series of dihydropyrano[2,3-c]pyrazole derivatives as a novel family of PPARγ partial agonists. Two analogues were found to display high affinity for PPARγ with potencies in the micro molar range.

LOX1 inhibition with small molecules.

Lipoxygenases (LOXs) are nonheme, iron-containing dioxygenases that catalyze the dioxygenation of polyunsaturated fatty acids and are widely distributed among plant and animal species. Human LOXs, now identified as key enzymes in the pathogenesis of major disorders, have increasingly drawn the attention as targets and great effort has been made for the discovery and design of suitable inhibitors, to which end both pharmacological and computational methods have been employed. In the present work, using pharmacophore modeling and docking, we attempt to elucidate the inhibition of LOX1 with a new inhibitor, albidoside, an iridoid glucoside isolated from plants of the Scutellaria genus.

The elusive endogenous adipogenic PPARγ agonists: Lining up the suspects.

The nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ) is the key decisive factor controlling the development of adipocytes. Ligand-mediated activation of PPARγ occurs early during adipogenesis and is thought to prime adipose conversion. Although several fatty acids and their derivatives are known to bind to and activate PPARγ, the identity of the ligand(s) responsible for initiating adipocyte differentiation is still a matter of debate.

Developing a molecular roadmap of drug-food interactions.

Recent research has demonstrated that consumption of food -especially fruits and vegetables- can alter the effects of drugs by interfering either with their pharmacokinetic or pharmacodynamic processes. Despite the recognition of such drug-food associations as an important element for successful therapeutic interventions, a systematic approach for identifying, predicting and preventing potential interactions between food and marketed or novel drugs is not yet available. The overall objective of this work was to sketch a comprehensive picture of the interference of ∼ 4,000 dietary components present in ∼1800 plant-based foods with the pharmacokinetics and pharmacodynamics processes of medicine, with the purpose of elucidating the molecular mechanisms involved.

NutriChem: a systems chemical biology resource to explore the medicinal value of plant-based foods.

There is rising evidence of an inverse association between chronic diseases and diets characterized by rich fruit and vegetable consumption. Dietary components may act directly or indirectly on the human genome and modulate multiple processes involved in disease risk and disease progression. However, there is currently no exhaustive resource on the health benefits associated to specific dietary interventions, or a resource covering the broad molecular content of food.

Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks.

Background: Epidemiological studies in the recent years have investigated the relationship between dietary habits and disease risk demonstrating that diet has a direct effect on public health. Especially plant-based diets -fruits, vegetables and herbs- are known as a source of molecules with pharmacological properties for treatment of several malignancies. Unquestionably, for developing specific intervention strategies to reduce cancer risk there is a need for a more extensive and holistic examination of the dietary components for exploring the mechanisms of action and understanding the nutrient-nutrient interactions.

Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level.

Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression.

Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics.

As both the amount of generated biological data and the processing compute power increase, computational experimentation is no longer the exclusivity of bioinformaticians, but it is moving across all biomedical domains. For bioinformatics to realize its translational potential, domain experts need access to user-friendly solutions to navigate, integrate and extract information out of biological databases, as well as to combine tools and data resources in bioinformatics workflows. In this review, we present services that assist biomedical scientists in incorporating bioinformatics tools into their research.

Discovery of a novel selective PPARγ ligand with partial agonist binding properties by integrated in silico/in vitro work flow.

Full agonists to the peroxisome proliferator-activated receptor (PPAR)γ, such as Rosiglitazone, have been associated with a series of undesired side effects, such as weight gain, fluid retention, cardiac hypertrophy, and hepatotoxicity. Nevertheless, PPARγ is involved in the expression of genes that control glucose and lipid metabolism and is an important target for drugs against type 2 diabetes, dyslipidemia, atherosclerosis, and cardiovascular disease. In an effort to identify novel PPARγ ligands with an improved pharmacological profile, emphasis has shifted to selective ligands with partial agonist binding properties.

ChemProt-2.0: visual navigation in a disease chemical biology database.

ChemProt-2.0 (http://www.cbs.

The chemical interactome space between the human host and the genetically defined gut metabotypes.

The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease).

Mapping the genome of Plasmodium falciparum on the drug-like chemical space reveals novel anti-malarial targets and potential drug leads.

The parasite Plasmodium falciparum is the main agent responsible for malaria. In this study, we exploited a recently published chemical library from GlaxoSmithKline (GSK) that had previously been confirmed to inhibit parasite growth of the wild type (3D7) and the multi-drug resistance (D2d) strains, in order to uncover the weak links in the proteome of the parasite. We predicted 293 proteins of P.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes.

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful.

Back to the Roots: Prediction of Biologically Active Natural Products from Ayurveda Traditional Medicine.

Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network.

Pharmacophore-driven identification of PPARγ agonists from natural sources.

In a search for more effective and safe anti-diabetic compounds, we developed a pharmacophore model based on partial agonists of PPARγ. The model was used for the virtual screening of the Chinese Natural Product Database (CNPD), a library of plant-derived natural products primarily used in folk medicine. From the resulting hits, we selected methyl oleanonate, a compound found, among others, in Pistacia lentiscus var.

Navigating the human metabolome for biomarker identification and design of pharmaceutical molecules.

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics.

ChemProt: a disease chemical biology database.

Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical-protein annotation resources, as well as disease-associated protein-protein interactions (PPIs).

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases.

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin.

Chemoinformatics-assisted development of new anti-biofilm compounds.

Bacterial biofilms are associated with a large number of infections. Biofilm-dwelling bacteria are particularly resistant to antibiotics, making it hard to eradicate biofilm-associated infections. Here, we use a novel cross-disciplinary approach combining microbiology and chemoinformatics to identify new and efficient anti-biofilm drugs.

Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification.

Background: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.

Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers.

Prediction of pH-dependent aqueous solubility of druglike molecules.

In the present work, the Henderson-Hasselbalch (HH) equation has been employed for the development of a tool for the prediction of pH-dependent aqueous solubility of drugs and drug candidates. A new prediction method for the intrinsic solubility was developed, based on artificial neural networks that have been trained on a druglike PHYSPROP subset of 4548 compounds. For the prediction of acid/base dissociation coefficients, the commercial tool Marvin has been used, following validation on a data set of 467 molecules from the PHYSPROP database.